Back ground: Skin grafting is the most common form
of reconstructive surgery, and regeneration of
sensations in skin grafts is a complex process
influenced by many factors such as , the thickness of
the graft, the depth of the grafted bed, meshing of the
graft, the condition of the bed and the surrounding
area. So many studies performed on this subject, some
of them clinically based on subjective type of sensation
tests, and others histological to detect the presence of
nerve fibers in the grafted skin
Objectives: To detect return of sensations to split
thickness skin grafts by clinical methods.
Methods: From Oct. 1995 to Oct. 2010, a clinical
prospective study performed in Al wasity Hospital for
reconstructive surgery, Hilla teaching General
Hospital, and Al kindy teaching General Hospital on
recovery of sensations in human split thickness skin
grafts on 200 patients, 400 grafts. There were 120
male, 80 female patients, there ages ranged from20 -61
years with mean of 28 years. The regeneration of
sensation of pain, touch, cold, and warmth, was studied
with the usual clinical methods. We studied; different
graft thicknesses, depth of graft beds, meshing of the
grafts, grafts on early and late wound excisions
Results: in our study the regeneration of sensations
occurred in the following order; pain, touch, cold ,
warmth, and has been found to extend over a period of
16days to 3 months, and sensations improve with time
but never recover completely even after several years.
Conclusion: The recovery of sensation of grafted skin
is a complex process that is influenced by many
factors; some of them are related to the graft, to the
recipient bed, to the patient as a whole, and occurs if
the graft is applied on a sufficiently innervated bed.
The goal (purpose) from using development technology that require mathematical procedure related with high Quality & sufficiency of solving complex problem called Dynamic Programming with in recursive method (forward & backward) through finding series of associated decisions for reliability function of Pareto distribution estimator by using two approach Maximum likelihood & moment .to conclude optimal policy
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